Renewable energy resource integrated multilevel inverter using evolutionary algorithms

Abstract

In this paper, with the development of an intelligent power system idea, sustainable energy sources were increasingly deployed, including transmission and distribution systems networks. As a result, optimal use of cascaded H-bridge inverter topologies (MLIs) and power distribution operations is critical for long-term power generation. Traditionally, selective harmonics reduction models must be performed to achieve the optimal switching frequency of multilevel inverters. This research aims to determine the switching frequency for wind-incorporated multilevel inverters to reduce overall harmonic components used in grid applications. This research adds towards the best possible solution by employing multiple newly established adaptive optimization techniques: MNSGA-II and salp swarm. The well-known genetic algorithm and particle swarm optimization are used for the wind-tied multilevel inverters optimization issue. Seven-level, eleven-level, and fifteen-level MLIs were employed to reduce overall harmonic distortion. The reliability and convergence rate of simulated data with various modulation indices for seven-, eleven-, and fifteen-level MLIs are obtained and compared. Models are developed based on MATLAB Simulink and are used to validate quantitative measurements.

Keywords

• Modified non-dominated sorting genetic algorithm (MNSGA-II)
• salp swarm algorithm (SSA)
• particle swarm optimization (PSO)
• multilevel inverters (MLIs)
• modulation index
• cascaded H-bridge